Category: Learning Levels

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses […]

Generative AI models for coding companions are mostly trained on publicly available source code and natural language text. While the large size of the training corpus enables the models to generate code for commonly used functionality, these models are unaware of code in private repositories and the associated coding styles that are enforced when developing […]

This post explores how Amazon CodeWhisperer can help with code optimization for sustainability through increased resource efficiency. Computationally resource-efficient coding is one technique that aims to reduce the amount of energy required to process a line of code and, as a result, aid companies in consuming less energy overall. In this era of cloud computing, […]

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. Queries is a feature that enables you to extract specific pieces of information from varying, complex documents using natural language. Custom Queries provides a way for you to customize the Queries feature for your business-specific, non-standard documents […]

We are excited to announce that Amazon SageMaker JumpStart can now stream large language model (LLM) inference responses. Token streaming allows you to see the model response output as it is being generated instead of waiting for LLMs to finish the response generation before it is made available for you to use or display. The […]

There’s a kind of magic that surrounds a soccer shot so powerful, it leaves spectators, players, and even commentators in a momentary state of awe. Think back to a moment when the sheer force of a strike left an entire Bundesliga stadium buzzing with energy. What exactly captures our imagination with such intensity? While there […]

Amazon SageMaker Canvas now supports deploying machine learning (ML) models to real-time inferencing endpoints, allowing you take your ML models to production and drive action based on ML-powered insights. SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate ML predictions for their business needs. Until now, SageMaker Canvas […]

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Combined with large language models (LLM) and Contrastive Language-Image Pre-Training (CLIP) trained with a large quantity of multimodality data, visual language models (VLMs) are particularly adept at tasks like image captioning, […]

Amazon Bedrock is a fully managed service provided by AWS that offers developers access to foundation models (FMs) and the tools to customize them for specific applications. It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. You can choose from various FMs from Amazon and leading […]

We are excited to announce a simplified version of the Amazon SageMaker JumpStart SDK that makes it straightforward to build, train, and deploy foundation models. The code for prediction is also simplified. In this post, we demonstrate how you can use the simplified SageMaker Jumpstart SDK to get started with using foundation models in just a couple of lines of code.